The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

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A robust, efficient, dynamic, and automated nudged elastic band (AutoNEB) algorithm to effectively locate transition states is presented. The strength of the algorithm is its ability to use fewer resources than the nudged elastic band (NEB) method by focusing first on converging a rough path before improving upon the resolution around the transition state. To demonstrate its efficiency, it has been benchmarked using a simple diffusion problem and a dehydrogenation reaction. In both cases, the total number of force evaluations used by the AutoNEB method is significantly less than the NEB method. Furthermore, it is shown that for a fast and robust relaxation to the transition state, a climbing image elastic band method where the full spring force, rather than only the component parallel to the local tangent to the path, is preferred especially for pathways through energy landscapes with multiple local minima. The resulting corner cutting does not affect the accuracy of the transition state as long as this is located with the climbing image method. Finally, a number of pitfalls often encountered while locating the true transition state of a reaction are discussed in terms of systematically exploring the multidimensional energy landscape of a given process.

One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes no prior data or knowledge on atomic interactions but inquires a first-principles quantum mechanical program for physical properties. Using reinforcement learning, the algorithm accumulates knowledge of chemical compound space for a given number and type of atoms and stores this in the neural network, ultimately learning the blueprint for the optimal structural arrangement of the atoms for a given target property. ASLA is demonstrated to work on diverse problems, including grain boundaries in graphene sheets, organic compound formation and a surface oxide structure. This approach to structure prediction is a first step toward direct manipulation of atoms with artificially intelligent first principles computer codes.

We show how to speed up global optimization of molecular structures using machine learning methods. To represent the molecular structures we introduce the auto-bag feature vector that combines: i) a local feature vector for each atom, ii) an unsupervised clustering of such feature vectors for many atoms across several structures, and iii) a count for a given structure of how many times each cluster is represented. During subsequent global optimization searches, accumulated structure-energy relations of relaxed structural candidates are used to assign local energies to each atom using supervised learning. Specifically, the local energies follow from assigning energies to each cluster of local feature vectors and demanding the sum of local energies to amount to the structural energies in the least squares sense. The usefulness of the method is demonstrated in basin hopping searches for 19-atom structures described by single-or double-well Lennard-Jones type potentials and for 24 atom carbon structures described by density functional theory (DFT). In all cases, utilizing the local energy information derived on-the-fly enhances the rate at which the global minimum energy structure is found. arXiv:1807.04605v2 [physics.comp-ph]